import transformers
from PIL import Image
import torch
import re
from .base import BaseModel
from ..dataset import DATASET_TYPE
from ..smp import cn_string, listinstr
import pandas as pd
import string
from typing import List


class POINTS(BaseModel):
    """Official implementation of POINTS: Improving Your Vision-language Model with Affordable Strategies # noqa

    Paper link: https://arxiv.org/abs/2409.04828
    POINTS is a vision-language model developed by researchers at WeChat AI. This model represents the inaugural version in our
    series of multimodal models, known as WePOINTS.

    Args:
        model_path (str): The path or the name (the unique huggingface id) of the model.
    """

    def __init__(self, model_path: str, **kwargs) -> None:
        from transformers import AutoModelForCausalLM, AutoTokenizer
        from transformers import CLIPImageProcessor

        version = transformers.__version__
        use_fast = True
        if "yi" in model_path.lower():
            assert (
                version == "4.38.2"
            ), f"The version of transformers for Yi-1.5 should be 4.38.2, but got {version}."  # noqa
            use_fast = False
        self.tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=use_fast)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_path, trust_remote_code=True, device_map="cuda"  # noqa
        ).to(torch.bfloat16)
        self.image_processor = CLIPImageProcessor.from_pretrained(model_path)

    def use_custom_prompt(self, dataset: str) -> bool:
        """Whether to use custom prompt for the dataset.

        Args:
            dataset (str): The name of the dataset.

        Returns:
            bool: Whether to use custom prompt for the dataset.
        """
        if DATASET_TYPE(dataset) == "MCQ":
            return True
        return False

    def build_prompt(self, line: str, dataset: str) -> List[dict]:
        """Build prompt for multi-choice dataset.

        Args:
            line (str): one line of the dataset.
            dataset (str): The name of the dataset.

        Returns:
            List[dict]: A list of elements constructed for current line.
        """
        assert self.use_custom_prompt(dataset)
        assert isinstance(dataset, str)
        tgt_path = self.dump_image(line, dataset)

        question = line["question"]
        hint = line["hint"] if ("hint" in line and not pd.isna(line["hint"])) else None
        if hint is not None:
            question = hint + "\n" + question

        options = {
            cand: line[cand]
            for cand in string.ascii_uppercase
            if cand in line and not pd.isna(line[cand])
        }
        for key, item in options.items():
            question += f"\n{key}. {item}"
        prompt = question

        if len(options):
            prompt += (
                "\n请直接回答选项字母。"
                if cn_string(prompt)  # noqa
                else "\nAnswer with the option's letter from the given choices directly."  # noqa
            )
        else:
            prompt += (
                "\n请直接回答问题。"
                if cn_string(prompt)  # noqa
                else "\nAnswer the question directly."
            )
        message = [dict(type="image", value=s) for s in tgt_path]
        message.append(dict(type="text", value=prompt))
        return message

    def generate_inner(self, message: List[dict], dataset: str = None) -> str:
        """Generate response for the given message.

        Args:
            message (List[dict]): A list of elements constructed for
                current line.
            dataset (str): The name of the dataset.

        Returns:
            str: The generated response.
        """
        prompt, image_path = self.message_to_promptimg(message)
        catty = True  # whether to use catty
        if dataset == "HallusionBench":
            prompt = (
                prompt
                + " Please answer yes or no. Answer the question using a single word or phrase."
            )  # noqa
        elif dataset == "MMVet":
            prompt = prompt + " Answer this question in detail."
            catty = False
        else:
            # use default setting
            pass

        if dataset is None:
            max_splits = 8
        elif listinstr(["MMBench", "OCRBench"], dataset):
            max_splits = 12
        else:
            max_splits = 8

        image = Image.open(image_path).convert("RGB")
        generation_config = {
            "max_new_tokens": 1024,
            "temperature": 0.0,
            "top_p": 0.0,
            "num_beams": 1,
        }
        response = self.model.chat(
            image,
            prompt,
            self.tokenizer,
            self.image_processor,
            catty,
            generation_config,
            max_splits,
        )
        return response


class POINTSV15(BaseModel):
    """Official implementation of POINTSv1.5

    This implementation is based on the official implementation of POINTSv1.5
    (https://github.com/WePOINTS/WePOINTS)

    Args:
        model_path (str): The path or the name (the unique huggingface id)
            of the model.
    """

    def __init__(self, model_path: str, **kwargs) -> None:
        from transformers import AutoModelForCausalLM, AutoTokenizer
        from transformers import QuantoConfig

        self.tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True
        )
        quant_config = QuantoConfig(modules_to_not_convert=["vision_encoder"])
        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            trust_remote_code=True,  # noqa
            device_map="cuda",
            torch_dtype=torch.bfloat16,
            quantization_config=quant_config,
        )
        try:
            from wepoints.utils.images import Qwen2ImageProcessorForPOINTSV15
        except ImportError:
            print(
                "Please install WePOINTS, and refer to https://github.com/WePOINTS/WePOINTS"
            )
        self.image_processor = Qwen2ImageProcessorForPOINTSV15.from_pretrained(
            model_path
        )  # noqa

    def use_custom_prompt(self, dataset: str) -> bool:
        """Whether to use custom prompt for the dataset.

        Args:
            dataset (str): The name of the dataset.

        Returns:
            bool: Whether to use custom prompt for the dataset.
        """
        if DATASET_TYPE(dataset) == "MCQ":
            return True
        return False

    def build_prompt(self, line: str, dataset: str) -> List[dict]:
        """Build prompt for multi-choice dataset.

        Args:
            line (str): one line of the dataset.
            dataset (str): The name of the dataset.

        Returns:
            List[dict]: A list of elements constructed for current line.
        """
        assert self.use_custom_prompt(dataset)
        assert isinstance(dataset, str)
        tgt_path = self.dump_image(line, dataset)

        question = line["question"]
        hint = line["hint"] if ("hint" in line and not pd.isna(line["hint"])) else None
        if hint is not None:
            question = hint + "\n" + question

        options = {
            cand: line[cand]
            for cand in string.ascii_uppercase
            if cand in line and not pd.isna(line[cand])
        }
        for key, item in options.items():
            question += f"\n{key}. {item}"
        prompt = question

        if len(options):
            prompt += (
                "\n请直接回答选项字母。"
                if cn_string(prompt)  # noqa
                else "\nAnswer with the option's letter from the given choices directly."  # noqa
            )
        else:
            prompt += (
                "\n请直接回答问题。"
                if cn_string(prompt)  # noqa
                else "\nAnswer the question directly."
            )
        message = [dict(type="image", value=s) for s in tgt_path]
        message.append(dict(type="text", value=prompt))
        return message

    def set_image_processor(self, dataset: str) -> None:
        """Set the image processor for the dataset.

        Args:
            dataset (str): The name of the dataset.
        """
        if dataset in ["OCRBench"]:
            self.image_processor.min_pixels = 280 * 280
        elif dataset in ["MMMU_DEV_VAL"]:
            self.image_processor.min_pixels = 1280 * 28 * 28
            self.image_processor.max_pixels = 16384 * 28 * 28
        elif dataset in ["MathVista_MINI"]:
            self.image_processor.min_pixels = 56 * 56
        elif dataset in [
            "MMVet",
            "HallusionBench",
            "MMBench_TEST_EN_V11",
            "MMBench_TEST_CN_V11",
        ]:
            self.image_processor.min_pixels = 1280 * 28 * 28
        else:
            self.image_processor.min_pixels = 840 * 840

    def construct_messages(self, prompt: str, image_paths: List[str]) -> List[dict]:
        """Construct messages for the given prompt and image paths.

        Args:
            prompt (str): The prompt for the generation.
            image_paths (List[str]): A list of image paths.

        Returns:
            List[dict]: A list of elements constructed for current line.
        """
        content = []
        for image_path in image_paths:
            content.append(dict(type="image", image=image_path))
        content.append(dict(type="text", text=prompt))
        messages = [{"role": "user", "content": content}]
        return messages

    def generate_inner(self, message: List[dict], dataset: str = None) -> str:
        """Generate response for the given message.

        Args:
            message (List[dict]): A list of elements constructed for
                current line.
            dataset (str): The name of the dataset.

        Returns:
            str: The generated response.
        """
        self.set_image_processor(dataset)
        prompt, image_paths = self.message_to_promptimg(message)
        image_paths = [image_paths]
        if dataset == "HallusionBench":
            prompt = (
                prompt
                + " Please answer yes or no. Answer the question using a single word or phrase."
            )  # noqa
        elif dataset == "MMVet":
            prompt = prompt + " Answer this question in detail."
        else:
            # use default setting
            pass
        pattern = r"<image \d+>"
        prompt = re.sub(pattern, "\n", prompt)
        messages = self.construct_messages(prompt, image_paths)

        generation_config = {
            "max_new_tokens": 1024,
            "temperature": 0.0,
            "top_p": 0.0,
            "num_beams": 1,
        }
        response = self.model.chat(
            messages, self.tokenizer, self.image_processor, generation_config
        )
        return response
